Evidence map›Paper›PMID 39253092›Full record

ArticleFrontiers in immunology2024

A comprehensive landscape analysis of autophagy in cancer development and drug resistance.

Yue Li, Yang Yin, Tong Zhang, Jinhua Wang, Zeqi Guo, Yuyun Li, Ya Zhao, Ruihong Qin, Qian He

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Proteostasis signatures in human diseases.PLoS computational biology · 2025
    Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yue LiDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yang YinDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Tong ZhangDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Jinhua WangDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Zeqi GuoDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yuyun LiDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ya ZhaoDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ruihong QinDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Qian HeDepartment of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Autophagy plays important roles in cancer progression and therapeutic resistance, and the autophagy underlying the tumor pathogenesis and further mechanisms of chemoresistance emergence remains unknown. Methods: In this study, via the single-sample gene set enrichment analysis (ssGSEA) method, an autophagy 45-gene list was identified to evaluate samples' autophagy activity, verified through six GEO datasets with a confirmed autophagy phenotype. It was further utilized to distinguish tumors into autophagy score-high and score-low subtypes, and analyze their transcriptome landscapes, including survival analysis, correlation analysis of autophagy- and resistance-related genes, biological functional enrichment, and immune- and hypoxia-related and genomic heterogeneity comparison, in TCGA pan-cancer datasets. Furthermore, we performed an analysis of autophagy status in breast cancer chemoresistance combined with multiple GEO datasets and Results: The 45-gene list was used to identify autophagy score-high and score-low subtypes and further analyze their multi-dimensional features. We demonstrated that cancer autophagy status correlated with significantly different prognoses, molecular alterations, biological process activations, immunocyte infiltrations, hypoxia statuses, and specific mutational processes. The autophagy score-low subtype displayed a more favorable prognosis compared with the score-high subtype, associated with their immune-activated features, manifested as high immunocyte infiltration, including high CD8+T, Tfh, Treg, NK cells, and tumor-associated macrophages M1/M2. The autophagy score-low subtype also showed a high hypoxia score, and hypoxic tumors showed a significantly differential prognosis in different autophagy statuses. Therefore, "double-edged" cell fates triggered by autophagy might be closely correlated with the immune microenvironment and hypoxia induction. Results demonstrated that dysregulated autophagy was involved in many cancers and their therapeutic resistance and that the autophagy was induced by the resistance-reversing drug response, in five breast cancer GEO datasets and validated by Conclusion: Our study provided a comprehensive landscape of the autophagy-related molecular and tumor microenvironment patterns for cancer progression and resistance, and highlighted the promising potential of drug-induced autophagy in the activation of drug sensitivity and reversal of resistance.

Indexed as

AutophagyDrug Resistance, NeoplasmGene Expression Regulation, NeoplasticAntineoplastic AgentsBiomarkers, TumorBreast NeoplasmsCell Line, TumorDatabases, GeneticFemaleGene Expression ProfilingHumansNeoplasmsPrognosisTranscriptomeTumor MicroenvironmentAntineoplastic AgentsBiomarkers, Tumorbreast cancerdoxorubicin resistanceimmune microenvironmentpan-cancertumor resistance

Identifiers

PMID39253092
PMCPMC11381251

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.